Accelerating Image-Sensor-Based Deep Learning Applications
| Autor: | Kevin Siu, Sayeh Sharify, Mostafa Mahmoud, Dylan Malone Stuart, Alberto Delmas Lascorz, Andreas Moshovos, Jorge Albericio, Isak Edo Vivancos, Patrick Judd, Zissis Poulos, Milos Nikolic |
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| Rok vydání: | 2019 |
| Předmět: |
Artificial neural network
business.industry Computer science Deep learning Inference 02 engineering and technology Machine learning computer.software_genre 020202 computer hardware & architecture Hardware and Architecture 0202 electrical engineering electronic engineering information engineering Artificial intelligence Electrical and Electronic Engineering Image sensor business computer Software |
| Zdroj: | IEEE Micro. 39:26-35 |
| ISSN: | 1937-4143 0272-1732 |
| DOI: | 10.1109/mm.2019.2930596 |
| Popis: | We review two inference accelerators that exploit value properties in deep neural networks: 1) Diffy that targets spatially correlated activations in computational imaging DNNs, and 2) Tactical that targets sparse neural networks using a low-overhead hardware/software weight-skipping front-end. Then we combine both into Di-Tactical to boost benefits for both scene understanding workloads and computational imaging tasks. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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